Best Practices
& Future of AI-Driven Development
Lecture 10
Industry patterns and what's coming next
2026 WayUp
AIDD Best Practices Summary
1. Vision First
Always create vision.md before coding. AI needs context to generate relevant code.
2. TDD Always
Write tests before implementation. Tests are your verification of AI correctness.
3. Review Everything
AI makes mistakes. Review generated code for bugs, security issues, and duplication.
4. Iterate Small
One task at a time. Small, focused changes are easier to verify and revert.
Security Best Practices
Never Trust Blindly
- AI can generate insecure code
- Check for SQL injection
- Verify authentication logic
- Review crypto implementations
Secrets & Credentials
- Never paste secrets in prompts
- Use environment variables
- Check for hardcoded credentials
- Review .gitignore coverage
Automated Scanning
- Run SAST tools (Snyk, SonarQube)
- Dependency vulnerability checks
- Pre-commit security hooks
High-Risk Reviews
- Auth code: human review required
- Payment logic: expert validation
- Compliance code: legal review
Team Best Practices
Shared Standards
- Common vision.md template
- Agreed prompt patterns
- Consistent tool stack
- Shared AI instructions file
- Code review guidelines for AI code
Knowledge Sharing
- Document effective prompts
- Share AI failure patterns
- Maintain prompt library
- Regular AI tool reviews
- Pair programming with AI
Pitfalls to Avoid
Over-Reliance
Using AI for everything without understanding the code. You must be able to maintain it.
Skipping Reviews
"AI wrote it, it must be correct." AI hallucinates, introduces bugs, and misses edge cases.
Context Neglect
Not providing project context. AI generates generic code that doesn't fit your architecture.
Test Avoidance
"AI code is good enough." Without tests, regressions are inevitable.
Near-Term Future (2026-2027)
Smarter Agents
Full codebase understanding, multi-repo awareness, autonomous PR creation
Better Context
Longer context windows (500K+ tokens), persistent memory across sessions
Specialized Models
Models fine-tuned for specific languages, frameworks, or codebases
The developer who knows how to effectively work with AI will outperform the one who doesn't, regardless of raw coding skill.
Long-Term Trends (2028+)
Natural Language Specs
Describe features in plain language, get production-ready implementations
Continuous AI Review
AI monitors production, suggests improvements, auto-fixes issues
AI-Native IDEs
Development environments built around AI collaboration from the ground up
Automated Testing
AI generates and maintains test suites, finds edge cases automatically
Skills for AI-Era Developers
More Important
- System design & architecture
- Requirement analysis
- Code review skills
- Prompt engineering
- Domain expertise
- Critical thinking
Less Differentiated
- Memorizing syntax
- Boilerplate coding
- Repetitive tasks
- Basic debugging
- Simple CRUD implementations
Career Advice for AI-Era Developers
Learn AI
Master AI tools now. They're not going away.
Go Deep
Specialize in areas AI struggles with.
Communicate
Translating business needs to tech is key.
Adapt
The tools will keep evolving. Stay curious.
Course Summary: AIDD Methodology
Vision
Document first: vision.md, specs, ADRs
Workflow
Discover → Plan → Review → Execute → Commit → Test
TDD
Tests first, implementation second
Prompts
Context + Task + Constraints + Output
Review
Human verification at every step
Iterate
Small cycles, continuous improvement
Final Thoughts
AI doesn't replace developers. It amplifies them. The developers who thrive will be those who can effectively direct AI agents, not those who memorize syntax.
Embrace AI
It's a tool, not a threat. Learn to use it well.
Stay Critical
AI makes mistakes. Your judgment is essential.
Keep Learning
The field evolves fast. Stay curious.
Thank You!
AI-Driven Development Course Complete
AIDD Framework: github.com/paralleldrive/aidd
2026 WayUp - way-up.io